Code
import numpy as np
a = np.arange(15).reshape(3, 5)
aarray([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
See Figure 1
import matplotlib.pyplot as plt
fig = plt.figure()
x = np.arange(10)
y = 2.5 * np.sin(x / 20 * np.pi)
yerr = np.linspace(0.05, 0.2, 10)
plt.errorbar(x, y + 3, yerr=yerr, label='both limits (default)')
plt.errorbar(x, y + 2, yerr=yerr, uplims=True, label='uplims=True')
plt.errorbar(x, y + 1, yerr=yerr, uplims=True, lolims=True,
label='uplims=True, lolims=True')
upperlimits = [True, False] * 5
lowerlimits = [False, True] * 5
plt.errorbar(x, y, yerr=yerr, uplims=upperlimits, lolims=lowerlimits,
label='subsets of uplims and lolims')
plt.legend(loc='lower right')
plt.show(fig)
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1699 | Zimbabwe | Africa | 1987 | 62.351 | 9216418 | 706.157306 | ZWE | 716 |
| 1700 | Zimbabwe | Africa | 1992 | 60.377 | 10704340 | 693.420786 | ZWE | 716 |
| 1701 | Zimbabwe | Africa | 1997 | 46.809 | 11404948 | 792.449960 | ZWE | 716 |
| 1702 | Zimbabwe | Africa | 2002 | 39.989 | 11926563 | 672.038623 | ZWE | 716 |
| 1703 | Zimbabwe | Africa | 2007 | 43.487 | 12311143 | 469.709298 | ZWE | 716 |
1704 rows × 8 columns
import plotly.express as px
import plotly.io as pio
gapminder = px.data.gapminder()
def gapminder_plot(year):
gapminderYear = gapminder.query("year == " +
str(year))
fig = px.scatter(gapminderYear,
x="gdpPercap", y="lifeExp",
size="pop", size_max=60,
hover_name="country")
fig.show()
gapminder_plot(1957)
gapminder_plot(2007)